NOAA-18 Instrument Calibration and Validation Briefing

1. NOAA-18 Instrument Calibration and Validation Briefing NOAA/NESDIS/Office of Research and Applications July 6, 2005 For archived activities and latest news, please visit http://www.orbit.nesdis.noaa.gov/smcd/spb/n18calval

2. Mitch Goldberg: ORA/SMCD Division Chief, - Management and Technical Oversight Fuzhong Weng: ORA/SMCD/Sensor Physics Branch Chief and NOAA-18 cal/val team leader, instrument asymmetry and microwave products and algorithms, radiance bias assessments for NWP model applications Changyong Cao: HIRS instrument calibration Fred Wu: AVHRR VIS/IR instrument calibration Tsan Mo: AMSU/MHS instrument calibration Jerry Sullivan: AVHRR thermal channel calibration/ NDVI validation Tony Reale: HIRS/AMSU/MHS sounding channel/products validation Mike Chalfant: HIRS/AMSU/MHS sounding channel/products validation /geolocation Ralph Ferraro: AMSU/MHS window channels/MSPPS products validation Larry Flynn: SBUV product validation Tom Kleespies: AMSU on-orbit verification Hank Drahos: Sounding product validation Dan Tarpley: AVHRR product NDVI monitoring John LeMarshall (JCSDA): Impacts assessments of NOAA-18 data for NWP applications Stephen English (UK): tests of direct readout data and NWP impacts demonstration Our Team

3. Outline for this Brief • ORA NOAA-18 Cal/Val Activities • Current Assessments • Summary • Next Step

4. Activities of the ORA NOAA-18 Cal/Val Team • Monitor and quantify instrument noises though analyzing calibration target counts and channel space view measurements • Assess instrument geolocation biases and co-registration and provide recommended solutions for satellite roll and pitch adjustments • Characterize other systematic biases in radiance through rigorous forward modeling and inter-satellite calibrations • Provide initial demonstration and assessments of NOAA-18 data for improving numerical weather prediction • Validate product algorithms (e.g. ATOVS and MSPPS, TOAST, UV index, NDVI, SST, AOD) for transition into operation • Communicate with NOAA-18 OV team, instrument vendors and users with timeliness diagnostics of instrument performances and provide root cause analyses

5. Long-Term Goals of ORA’s Cal/Val Program • Develop an Integrated Cal/Val System Framework to enhance ORA’s capability and efficiency to provide outstanding calibration and validation to METOP, NPP NPOESS, and GOES-R • Outcome >>> Provide timely and accurate assessments of NOAA instrument’s on-orbit performances and the impacts of noise and loss of channels on operational products and data assimilation

6. NOAA-18 Instrument Assessments • AVHRR/3 Advanced Very High Resolution Radiometer • HIRS/4 High Resolution Infrared Sounder • AMSU-A Advanced Microwave Sounding Unit-A • MHS Microwave Humidity Sounder • SBUV/2 Solar Backscattered Ultraviolet Radiometer

7. G G G Y Y Y R R R ORA NOAA-18 Instrument Cal/Val Status Report Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration Navigation errors are more than one scan lines and/or fovs and their causes have not been identified All channels do not meet the noise specification, or large unknown biases and anomaly Some channels do not meet the noise specification, no major anomaly and biases Small navigation errors which can be fixed through recommended roll and pitch adjustments All channels meet the noise specification No navigation errors NWP Readiness and Product Demo Analysis and Recommended Solutions Measurements are assessed and not ready for product and NWP applications No analyses and recommended solutions for out-of spec instrument parameters R Analysis for out-of spec parameters but solutions have not been recommended Y Some channels are assessed and may be useful for product and NWP applications Analysis and recommended solutions are proposed for out-of spec instrument parameters G Measurements are fully tested and ready for product and NWP applications

8. G Y Y R ORA NOAA-18 HIRS/4 Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration • HIRS/4 noise is fluctuating at 2-3 times the specification for most long-wave channels. • The noise among all channels is highly correlated • Space view count at channel 1 remains saturated and the noise has not been quantified • HIRS/4 geolocation has a mean shift of 3-4 AVHRR pixels, which is more related to AVHRR geolocation error NWP Readiness and Product Demo Analysis and Recommended Solutions • ORA has developed a website for real-time monitoring of noise trend • ORA had several telecons with OV team and instrument vendor to present the noise assessment results • ORA proposed several possible coherent noise generators, 1) clamp system itself, 2) interference with the clamp system or 3) through the clamp system • These root cause analyses are likely confirmed by MIT/Lincoln Lab. • Coefficients in radiative transfer model and data assimilation system are updated • Brightness temperatures at most of HIRS channels are not ready for sounding and NWP applications

9. G Y Y R Vch V0 ORA NOAA-18 HIRS/4 Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration HIRS Pixel Number NWP Readiness and Product Demo Analysis and Recommended Solutions Reference voltage for the clamp target which may be fluctuating

10. G G G Y ORA NOAA-18 AMSU-A Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration • AMSU-A noise is evaluated and shows all channels meet the specification. • Overall AMSU calibration algorithms are healthy with reasonable gains, and variability in cold and warm calibration targets • AMSU-A1 module displays a slightly larger cross-track asymmetry • AMSU-A1 is correctly navigated • AMSU-A2 is off by 0.7 MHS scanline along track and 0.36 MHS FOVs across track. Analysis and Recommended Solutions NWP Readiness and Product Demo • Coefficients in radiative transfer model and data assimilation system are updated • Impacts of AMSU on weather prediction are being assessed • AMSU microwave surface and precipitation products are demonstrated • Recommend to OV team for possible roll and pitch adjustments to AMSU-A2 module navigation errors • Recommend to OV team to investigate AMSU-A1 cross-track asymmetry with various antenna pattern correction algorithms

11. G G G Y ORA NOAA-18 AMSU-A Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration NWP Readiness and Product Demo Analysis and Recommended Solutions A1 cross-track asymmetry

12. G G G Y ORA NOAA-18 MHS Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration • Display 0.9 MHS scanline error along track • MHS noises are quantified and show all channels meet the specification and are better than AMSU-B NWP Readiness and Product Demo Analysis and Recommended Solutions • Coefficients in radiative transfer model and data assimilation system are updated • Impacts of MHS on weather prediction are being assessed • MHS microwave surface and precipitation products are demonstrated • Recommend to OV team for a possible roll adjustment to correct MHS navigation errors

13. G G G Y ORA NOAA-18 MHS Cal/Val Findings Support to NOAA’s Critical Satellite Program Noise, Bias and Anomaly Navigation and Co-registration MHS channel 1 reveals a coast line shift along track NWP Readiness and Product Demo Analysis and Recommended Solutions A high quality of rainfall distribution from MHS

14. ORA NOAA-18 AVHRR/3 Cal/Val Findings Support to NOAA’s Critical Satellite Program G G G Y Noise, Bias and Anomaly Navigation and Co-registration • The blackbody temperature changes are monitored and shows in a small range of variability (only 2K) • Thermal channel (3-5) calibration is healthy • Wu’s Vis/IR calibration algorithms are working well • AVHRR/3 has possible 2-3 pixels navigation errors along track NWP Readiness and Product Demo Analysis and Recommended Solutions • AVHRR/3 produces a good quality of NDVI • AVHRR/3 produces SST and aerosol optical depth with smaller biases • Recommend to OV team to have a possible roll adjustment

15. ORA NOAA-18 AVHRR/3 Cal/Val Findings Support to NOAA’s Critical Satellite Program G G G Y Noise, Bias and Anomaly Navigation and Co-registration AVHRR NDVI product shows AVHRR has a 2-3 pixels shift in coastline along track NWP Readiness and Product Demo Analysis and Recommended Solutions NOAA-18 AVHRR derived NDVI is based on NOAA-16 vicarious calibration algorithm. This NDVI product is selected for our initial assessments of visible channel calibration because it uses both visible/near infrared channel data.

16. Responses from Program Managers From Stephen English, The Metoffice UK: “Thanks for sending this Fuzhong. For your information we intend to put NOAA-18 "passively" into a parallel suite next week which will allow us to characterise bias characteristics more thoroughly. It will go active in the parallel suite in two weeks time (all being well) and fully operational in 4 weeks time. It seems to us that NOAA-18 is in pretty good shape except for HIRS (which we won't use initially) and the issues which are being discussed are mostly what could be described as fine-tuning. When it goes operational we will drop Aqua AMSU-A from our assimilation system”. “Thanks for this. We will try and put together a report today on our monitoring of NOAA-18 against our model and send it to you to use as you see fit in NOAA. We won't distribute to anyone else. As far as we are concerned AMSU-A and MHS data quality is good enough to assimilate with our normal bias correction and quality control procedures in place. We will be spinning up bias corrections over the next 2 weeks and putting it into a parallel suite in two weeks time which will run for several weeks. We are disappointed that we are still being told not to pass the data to ECMWF and Meteo-France. It would be useful to see their monitoring of it” From Jim Silva, NDE Program Manager: “The attached analysis of the NOAA 18 HIRS and AMSUA data anomalies demonstrates the level of expertise available within our Office of Research and Application to investigate sensor problems with instrument manufacturers, test hypotheses, and find acceptable work-arounds” From Bill Mazur, NESDIS Polar Satellite Acquisition Manager: “Excellent work by your folks, Fuzhong.  Thanks for including me in the distribution” From Tom Schott, NESDIS Polar Satellite Product Manager: “Graeme,Here's some more initial finding from ORA on MHS” From Al Goldberg, IPO: “Jim,Thanks for forwarding this to me.  It looks like very good work, and highlights the types of analytical tools, approaches, and experience reserves which will be irreplaceable if we expect to get good performance from the NPP/NPOESS sensors.  I'll forward the presentation to Karen and Joe Mulligan”……….

17. Overall Summary • Most instruments are assessed under ORA cal/val project and their noises have met specification with the current exception of HIRS • Navigation and co-registration errors for all instruments are examined and quantified. The required roll and pitch adjustments are recommended to OV team • AMSU instrument cross-track biases are quantified and A1 asymmetry is being studied • NOAA-18 AVHRR NDVI product appears to be consistent with NOAA-16 • The NOAA-18 MHS NET for each channel was calculated and is better than AMSU-B by a factor of ~2 • AMSU-A/MHS hydrological parameters (rainfall, total precip. water) appear to be better quality • ORA has delivered all required software and codes to NCEP for assimilating all NOAA-18 measurements

18. Next Step • Develop an integrated calibration and validation system for timely and accurate calibration analyses for POES instrument on-orbit performances • Validate all existing operational products: clouds, aerosol, NDVI, SST, ATOVS, MSSPS, TOAST, SBUV • Study NOAA-18 unique products improvements (e.g. HIRS 10 km sounding, MHS cloud ice and rainfall products) • Test ORA Multi-sensor Integrated Retrieval system with NOAA-18 measurements

19. Backup Slides show ORA Long-Term Strategy for An Integrated Cal/Val System Framework

20. ORA Pre-launch Calibration Support Level 0 (RDR) Level 1B (SDR) Calibration Radiances from Earth Target Counts from Earth Targets Sensor Calibration Counts from Calibration Targets (ICT, SD, SPACE) Calibration Coefficients Meta Data to CLASS Long Term Monitoring Re-processing Pre-launch Sensor Characterization SOH Telemetry

21. Pre-launch Sensor Spectral Response Radiosonde and other surface measurements at selected sites Numerical weather prediction model outputs Level 0 and 1B data End-to-end simulator including RT model, multiple scattering from surface/atmosphere, FOV matching Proxy sensor data from POES and EOS Sensor noises, spacecraft data Vicarious calibration coefficients, Validated product algorithm coefficients, Geolocation and coregistration adjustments Noise trending monitoring, Transition into operation Onboard calibrated sensor data ORA Post-Launch Integrated Cal/Val System Framework

22. Cal/Val System Performance Example: Detecting DMSP SSMIS Anomaly Shown is the difference between simulated and observed SSMIS 53.6 GHz. The SSMIS is the first conical microwave sounding instrument, precursor of NPOESS CMIS. The calibration of this instrument remains unresolved after 2 years of the lunch of DMSP F16. The outstanding anomalies have been identified from three processes: 1) antenna emission after satellite out of the earth eclipse which contaminates the measurements in ascending node and small part in descending node, 2) solar heating to the warm calibration target and 3) solar reflection from canister tip, both of which affect most of parts of descending node.

23. Cal/Val System Performance Example: NOAA-16 AVHRR Calibration Slope Update Shown is NOAA-16 AVHRR channel 1slope calibration coefficient during the past three years. This calibration is done through a vicarious technique because there is no on-board visible channel calibration target for AVHRR

24. Cal/Val System Performance Example: WindSat Stokes channel Biases Shown are the time series of the mean 4th Stokes parameters over Amazon rainforests. the line with diamond, triangle and square corresponds to 10.7, 18.7 and 37 GHz, respectivelyMonthly mean of 4th Stokes components over Amazon rainforests should be zero because of surface roughness and heterogeneity relative to azimuthal direction. The residual of this mean is largely due to the instrument calibration biases. The bias (-0.5K) at 18.7 GHz will result in substantial bias in wind direction retrievals because of the actual wind induced signal is on the order of a couple of degrees in Kelvin

25. Transition to NPP/NPOESS Cal/Val NPOESS verification and validation. After the delivery of the NPP/NPOESS ground systems and with the launch of the NPP and NPOESS satellites, algorithm verification and product validation will berequired to ensure that data and products that meet spec are being delivered from the IDPS. The government will have to evaluate and make a determination of the quality of NPP and NPOESS EDRs. This job must be done in NESDIS by ORA and resources must be available to do it. There has been no commitment from IPO to fund this task. Without this funding, NESDIS will take delivery on the IDPS system and EDRs without independent verification of product accuracy or if the data stream and products even meet spec.